MEASURING SEDENTARY BEHAVIOUR IN PEOPLE WITH BACK PAIN: A SYSTEMATIC REVIEW
Bibliographic record
Abstract
Background and purpose To identify methods used to measure free living sedentary behaviour in people with back pain and review the validity and reliability of identified measures. Methods Databases including CINAHL, EMBASE, MEDLINE, AMED, PsycINFO, SPORTDiscus and the Sedentary Behaviour and Research Network website (www.sedentarybehaviour.org) were searched for relevant published articles up to June 2014. Studies which measured sedentary behaviour in people with back pain were included. Quality of the included studies was assessed using the Newcastle Ottawa Scale. The Consensus-based Standards for the Selection of Measurement Instruments (COSMIN) Checklist was used to assess psychometric properties. Results Six papers were identified; two of high methodological quality. The most common method of data collection was self-report, using activity diaries or questionnaires. Sedentary behaviour measured by accelerometry ranged from 6.7 to 10.7 hours per day whereas results from self-report measures ranged from 5 to 9.4 hours per day. According to the COSMIN checklist, the psychometric properties of the measurement instruments were rated fair to excellent. Conclusion People with back pain spend a large proportion of their waking day participating in sedentary behaviour. Therefore valid and reliable sedentary behaviour measurements, such as those identified in this study, are essential for assessing the effectiveness of public health interventions and for future population monitoring. Conflicts of interest: No conflicts of interest Sources of funding: Department for Employment and Learning
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".